What Are the Reasons Behind the Growing Popularity of Python?

We are in a strange juncture as far as the development of data science as a commercial weapon is concerned. The advent of data science from a research oriented academic background right into the heart of industrial development is history. This advancement in business procedures due to the introduction of data science somehow complicated things for a lot of enterprises. As the amount of data to account for grew exponentially, things kept getting harder. With all this happening a business tends to lose its focus on the simple motive of the whole thing which is essentially reducing risk and making more profit. I am deliberately focusing on the very materialistic usage of data science because this kind of usage is more prolific than any other sort.

Any ways, while the basic philosophy behind integrating data science in main stream business is quite simple, the process often serves to destroy the simplicity. It is a complex subject after all. Now, that data analytics and advanced analytics have become household terms, enterprises are inclined toward unscrambling complex things. Hence we have an encouraging forecast that the data science platform market is going to grow at 38.9% Compound Annual Growth Rate. The Python data science system is making an impression in these times, a very deep impression indeed; it is developed with simplicity and flexibility in mind. Good news for the aspirating data science professionals is that the number of skilled Python operators is hardly keeping pace with the growing popularity of Python. Let us see why.

Famous among the universities

A 2014 report suggests that Python was the most frequently taught programming language in the top US Universities as a part of the computer science course. In the context of 2017, the fact of Python’s being so famous among the university is completely justified. The last couple of years have been marked with crazy integration of big data and data science. While the most used programming language by data analysts and data scientists has been R for a while, Python has drawn a lot of users towards itself. With a swarm of new people coming into data intensive professions, Python data science practices have grown exponentially.

The ease of use

If something is becoming famous, there must be some fundamental reason behind it, something that one can see from outside without much technical understanding. In the case of Python it is definitely the simplicity that programmers readily associate to Python. It is easy to download and install and does not make you break a sweat to set it up. The best part yet is that it uses normal numeric and linguistic terms to write code. It is like programming in a known language just with a different way of using the words. It takes less amount of code to exert the same results than C++ or Java. It looks good if you compare it to other popular languages.

Python as a language was created keeping the non- programmer in mind although with all the capacities of a high functioning programming language. Professionals and enterprises have welcomed with arms wide open.

Fast and resourceful

The Python libraries are extensive and provide an immense support for the data scientists. This is one of the major reasons behind its being the most loved language. The libraries make it easier to apply a lot of machine learning algorithms or statistical analysis. They often save you from writing long codes. This plays a great part in its efficiency and cost effectiveness. The high functioning language is one of the fastest too; it will get your job done in a comparatively short time. The libraries also feature certain data visualization, data mining and natural speech recognition tools. All of these make Python data science way moiré attractive than many other systems.

Empowering frameworks

Python features a number of intelligently engineered frame works in order to enable the users use it in accordance to their specific needs and to write applications in short time. It basically looks after the comfort and convenience of the users. Some such frameworks are Django, Flask, Pyramid, Tornado etc.

The community

Learning a programming language attaches you with a lot of people who happen to be the practitioners of the same language. You can always depend on the experienced professionals to show some light when you are stuck. In the case of Python this community of people is very strong and helpful. Since it is an open source software system, there is a lot of constructive discussions happening on the forums. You just need to look around and you will find solutions.

All of these join forces to make the practice of Python data science a fairly opportune path for a progressive career in data science.